Use of online water quality monitoring for assessing the effects of WWTP overflows in rivers.

The effects on river water quality of sewer overflows are not well known. Since the duration of the overflow is in the order of magnitude of minutes to hours, continuous measurements of water quality are needed and traditional grab sampling is unable to quantify the pollution loads. The objective of this paper was to demonstrate the applicability of high frequency measurements for assessing the impacts of waste water treatment plants on the water quality of the receiving surface water. In our in situ water quality monitoring setup, two types of multiparameter sensors mounted on a floating fixed platform were used to determine the dynamics of dissolved oxygen, specific conductivity, ammonium-N, nitrate-N and dissolved organic carbon downstream of a waste water treatment plant (WWTP), in combination with data on rainfall, river discharge and WWTP overflow discharge. The monitoring data for water quantity and water quality were used to estimate the pollution load from waste water overflow events and to assess the impact of waste water overflows on the river water quality. The effect of sewer overflow on a small river in terms of N load was shown to be significant. The WWTP overflow events accounted for about 1/3 of the river discharge. The NH4-N loads during overflow events contributed 29% and 21% to the August 2010 and June 2011 load, respectively, in only 8% and 3% of the monthly time span. The results indicate that continuous monitoring is needed to accurately represent the effects of sewer overflows in river systems.

A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression.

INTRODUCTION: Tacrolimus is an important immunosuppressive drug for organ transplantation patients. It has a narrow therapeutic range, toxic side effects, and a blood concentration with wide intra- and interindividual variability. Hence, it is of the utmost importance to monitor tacrolimus blood concentration, thereby ensuring clinical effect and avoiding toxic side effects. Prediction models for tacrolimus blood concentration can improve clinical care by optimizing monitoring of these concentrations, especially in the initial phase after transplantation during intensive care unit (ICU) stay. This is the first study in the ICU in which support vector machines, as a new data modeling technique, are investigated and tested in their prediction capabilities of tacrolimus blood concentration. Linear support vector regression (SVR) and nonlinear radial basis function (RBF) SVR are compared with multiple linear regression (MLR). METHODS: Tacrolimus blood concentrations, together with 35 other relevant variables from 50 liver transplantation patients, were extracted from our ICU database. This resulted in a dataset of 457 blood samples, on average between 9 and 10 samples per patient, finally resulting in a database of more than 16,000 data values. Nonlinear RBF SVR, linear SVR, and MLR were performed after selection of clinically relevant input variables and model parameters. Differences between observed and predicted tacrolimus blood concentrations were calculated. Prediction accuracy of the three methods was compared after fivefold cross-validation (Friedman test and Wilcoxon signed rank analysis). RESULTS: Linear SVR and nonlinear RBF SVR had mean absolute differences between observed and predicted tacrolimus blood concentrations of 2.31 ng/ml (standard deviation [SD] 2.47) and 2.38 ng/ml (SD 2.49), respectively. MLR had a mean absolute difference of 2.73 ng/ml (SD 3.79). The difference between linear SVR and MLR was statistically significant (p < 0.001). RBF SVR had the advantage of requiring only 2 input variables to perform this prediction in comparison to 15 and 16 variables needed by linear SVR and MLR, respectively. This is an indication of the superior prediction capability of nonlinear SVR. CONCLUSION: Prediction of tacrolimus blood concentration with linear and nonlinear SVR was excellent, and accuracy was superior in comparison with an MLR model.

Four-year follow-up of infliximab therapy in rheumatoid arthritis patients with long-standing refractory disease: attrition and long-term evolution of disease activity.

Although there is strong evidence supporting the short-term efficacy and safety of anti-tumour necrosis factor-alpha agents, few studies have examined the long-term effects. We evaluated 511 patients with long-standing refractory rheumatoid arthritis treated with intravenous infusions of infliximab 3 mg/kg at weeks 0, 2, 6, and 14 and every 8 weeks thereafter for 4 years. Among the initial 511 patients included in the study, 479 could be evaluated; of these, 295 (61.6%) were still receiving infliximab treatment at year 4 of follow-up. The most common reasons for treatment discontinuation were lack of efficacy (65 patients, 13.6%), safety (81 patients, 16.9%), and elective change (38 patients, 7.9%). Analysis of disease activity scores (DAS28 [disease activity score based on the 28-joint count]) over time showed that, after the initial rapid improvement during the first 6 to 22 weeks of therapy, a further decrease in disease activity of 0.2 units in the DAS28 score per year was observed. DAS28 scores, measured at week 14 or 22, were found to predict subsequent discontinuation due to lack of efficacy. In conclusion, long-term maintenance therapy with infliximab 3 mg/kg is effective in producing further reductions in disease activity. Disease activity measured by the DAS28 at week 14 or 22 of infliximab therapy was the best predictor of long-term attrition.

DAS28 best reflects the physician's clinical judgment of response to infliximab therapy in rheumatoid arthritis patients: validation of the DAS28 score in patients under infliximab treatment.

This study is based on an expanded access program in which 511 patients suffering from active refractory rheumatoid arthritis (RA) were treated with intravenous infusions of infliximab (3 mg/kg+methotrexate (MTX)) at weeks 0, 2, 6 and every 8 weeks thereafter. At week 22, 474 patients were still in follow-up, of whom 102 (21.5%), who were not optimally responding to treatment, received a dose increase from week 30 onward. We aimed to build a model to discriminate the decision to give a dose increase. This decision was based on the treating rheumatologist's clinical judgment and therefore can be considered as a clinical measure of insufficient response. Different single and composite measures at weeks 0, 6, 14 and 22, and their differences over time were taken into account for the model building. Ranking of the continuous variables based on areas under the curve of receiver-operating characteristic (ROC) curve analysis, displayed the momentary DAS28 (Disease Activity Score including a 28-joint count) as the most important discriminating variable. Subsequently, we proved that the response scores and the changes over time were less important than the momentary evaluations to discriminate the physician's decision. The final model we thus obtained was a model with only slightly better discriminative characteristics than the DAS28. Finally, we fitted a discriminant function using the single variables of the DAS28. This displayed similar scores and coefficients as the DAS28. In conclusion, we evaluated different variables and models to discriminate the treating rheumatologist's decision to increase the dose of infliximab (+MTX), which indicates an insufficient response to infliximab at 3 mg/kg in patients with RA. We proved that the momentary DAS28 score correlates best with this decision and demonstrated the robustness of the score and the coefficients of the DAS28 in a cohort of RA patients under infliximab therapy.